Franciszek Hennel

Risk attitudes—the willingness to accept uncertainty for larger potential rewards—are often treated as a stable individual trait, yet people’s risky choices change markedly across contexts and even across repetitions of identical decisions. Here we show, using modelling and neuroimaging, that such rapid changes emerge from Bayesian inference on noisy magnitude representations in parietal cortex. Participants underwent fMRI while choosing between safe and risky options that are either held in working memory or shown on screen. Options held in working memory are noisier and more prone to central-tendency biases, so relatively small payoffs are overestimated and large payoffs underestimated, shifting choices between risk-seeking and risk aversion. A computational model of payoff perception captures these effects mechanistically. Numerical population-receptive-field modelling shows that when intraparietal payoff representations are noisier, choices are less consistent and less risk-neutral. Thus, risk attitudes and their contextual variability can be rooted in perceptual inference on parietal magnitude representations.

Introduction: The intra-voxel incoherent motion (IVIM) effect is an additional signal attenuation in in-vivo diffusion-weighted MRI due to blood microcirculation. The dependence of this effect on diffusion time is of high interest because it may inform about the architecture of blood vessels in perfusion studies and help optimizing diffusion scans to avoid the IVIM bias. This dependence is derived here using capillary blood trajectories obtained by realistic flow simulations in capillary bed samples of mouse brain cortex derived from two-photon microscopy. Methods: Using the gaussian phase approximation, NMR signal attenuation due to IVIM is derived for pulsed-gradient diffusion sequences at different diffusion times and for sequences with flow-compensated gradient shapes by calculating the product of the gradient moment spectra with the spectral density of the simulated blood velocity autocorrelation function. Results and Discussion: The results suggest that the microcirculation underlying the IVIM effect in the brain cannot be faithfully depicted by the commonly used models of diffusive or ballistic flow, but rather as a correlated random motion with a correlation time in the range of 60–120 ms. Consequently, the correlation time appears to be an interesting, MRI-accessible endpoint to characterize microcirculation. Further, the flow compensation as a means of suppressing the IVIM effect in diffusion measurements may not be fully effective.

The intra-voxel incoherent motion (IVIM) effect is an additional signal attenuation in in-vivo diffusion-weighted MRI due to blood microcirculation. The dependence of this effect on diffusion time is of high interest because it may inform about the architecture of blood vessels in perfusion studies and help optimizing diffusion scans to avoid the IVIM bias. This dependence is derived here using capillary blood trajectories obtained by realistic flow simulations in capillary bed samples of mouse brain cortex derived from two-photon microscopy. Using the gaussian phase approximation, NMR signal attenuation due to IVIM is derived for pulsed-gradient diffusion sequences at different diffusion times and for sequences with flow-compensated gradient shapes by calculating the product of the gradient moment spectra with the spectral density of the simulated blood velocity autocorrelation function. The results suggest that the microcirculation underlying the IVIM effect cannot be faithfully depicted by the commonly used models of diffusive or ballistic flow, but rather as a correlated random motion with a correlation time in the range of 60–120 milliseconds. Consequently, the correlation time appears to be an interesting, MRI-accessible endpoint to characterize microcirculation. Further, the flow compensation as a means of suppressing the IVIM effect in diffusion measurements may not be fully effective.

Purpose: MRI gradients with a conventional, bipolar design generally face a trade‐off among performance, encoding ambiguity, and radiofrequency selectivity used to circumvent said ambiguity. This problem is particularly limiting in cutting‐edge brain imaging performed at field strengths ≥ 7 T and using high‐performance head gradients. Methods: To address this issue, the present work proposes to fundamentally eliminate the encoding ambiguity in head gradients by using a unipolar z‐gradient design that takes advantage of the signal‐free range on one side of the imaging volume. This concept is demonstrated by implementation of a unipolar head gradient for operation at 7 T . Results: Imaging in phantoms and in vivo demonstrates elimination of backfolding due to encoding ambiguity. At the same time, the unipolar design achieves efficiency on par with conventional bipolar design, resulting in high amplitude and slew‐rate performance. Conclusion: The prospect of gradient systems based on a unipolar design holds promise for all advanced neuroimaging that demands high gradient performance. It will make the greatest difference at 7 T and beyond, where the absence of ambiguity removes a key concern and constraint in terms of radiofrequency behavior and instrumentation.

Purpose: To devise a more comprehensive quantitative representation for spectral encodings in frequency‐dependent diffusion measurements for improved estimation of D(ω).Theory and Methods: Whereas a spectral diffusion measurement is typically represented by a Dirac delta function at a single attributed frequency, spectral response is represented here by the encoding power in |Q(ω)|² over a set of contiguous frequency intervals. Using this representation paradigm, a linear encoding model is formulated wherein diffusivity over each interval can be estimated by inverting the encoding process from a set of measurements. This strategy was validated in in vivo human brain imaging experiments evaluating D(ω) up to 50 Hz over 10‐Hz intervals using high‐performance gradients. The employed spectral encodings were selected using an accompanying framework devised to ensure robust encoding performance given the chosen frequency intervals. Additionally, simulated measurements were carried out to compare the performance in estimating D(ω) using the proposed encoding model versus using single‐frequency attribution in relation to the form of D(ω) and the width of frequency intervals.Results: In vivo D(ω) determined using the proposed encoding strategy were found to increase with increasing frequency and could be mapped to spectral responses more spectrally selective than those characteristic of single‐frequency attribution. In turn, simulated measurements demonstrated that the linear encoding model permitted D(ω) estimation with improved accuracy, especially for more nonlinear D(ω), at the expense of reduced precision, particularly for narrower frequency intervals.Conclusion: By devising a more holistic representation paradigm for frequency‐dependent diffusion measurements, D(ω) can be recovered with higher fidelity.

Purpose: To investigate the time and frequency dependence of diffusion kurtosis in the in vivo human brain with pulsed (PGSE) and oscillating gradient spin‐echo (OGSE) experiments over a range of diffusion times and frequencies using high‐performance gradients.Methods: Four OGSE waveforms probing centroid frequencies of 11, 20, 34, and 51 Hz, corresponding to equivalent effective diffusion times of 22.7, 12.5, 7.4, and 4.9 ms; and three PGSE waveforms probing effective diffusion times of 50, 22.7, and 12.5 ms were designed for experimentation. Seven healthy volunteers were scanned using said waveforms at b‐values of 500, 1000, and 2000 s/mm². Kurtosis metrics were computed and evaluated in various white and gray matter regions of interest. Complementary Monte‐Carlo simulations were performed using a simple substrate with varying permeabilities and the same gradient waveforms as used for in vivo imaging.Results: Non‐monotonic, biphasic kurtosis time‐ and frequency‐dependences were observed in different white and gray matter regions. This behavior supports the notion that kurtosis is influenced by the combined effects of water‐restriction interactions, inter‐compartmental water exchange, and tissue heterogeneity. Kurtosis values measured using OGSE and PGSE waveforms at the same effective diffusion times showed significant discrepancies, with consistently higher values from PGSE than OGSE. These discrepancies are mainly attributed to differences in exchange sensitivities among gradient waveforms based on analogous findings from Monte‐Carlo simulations.Conclusion: Kurtosis was found to exhibit biphasic time‐ and frequency‐dependence in the human brain. When interpreting kurtosis measurements, the influence of exchange sensitivities of employed gradient waveforms should be considered.

Oscillating gradient spin echo (OGSE) diffusion MRI (dMRI) can probe the diffusive dynamics on short time scales ≲10 ms, which translates into the sensitivity to tissue microstructure at the short length scales m. OGSE‐based tissue microstructure imaging techniques able to characterize the cell diameter and cellular density have been established in pre‐clinical studies. The unique image contrast of OGSE dMRI has been shown to differentiate tumor types and malignancies, enable early diagnosis of treatment effectiveness, and reveal different pathophysiology of lesions in stroke and neurological diseases. Recent innovations in high‐performance gradient human MRI systems provide an opportunity to translate OGSE research findings in pre‐clinical studies to human research and the clinic. The implementation of OGSE dMRI in human studies has the promise to advance our understanding of human brain microstructure and improve patient care. Compared to the clinical standard (pulsed gradient spin echo), engineering OGSE diffusion encoding for human imaging is more challenging. This review summarizes the impact of hardware and human biophysical safety considerations on the waveform design, imaging parameter space, and image quality of OGSE dMRI. Here we discuss the effects of the gradient amplitude, slew rate, peripheral nerve stimulation, cardiac stimulation, gradient driver, acoustic noise and mechanical vibration, eddy currents, gradient nonlinearity, concomitant gradient, motion and flow, and signal‐to‐noise ratio. We believe that targeted engineering for safe, high‐quality, and reproducible imaging will enable the translation of OGSE dMRI techniques into the clinic.

Purpose: Field monitoring using field probes allows for accurate measurement of magnetic field perturbations, such as from eddy currents, during MRI scanning. However, errors may result when the spatial variation of the fields is not well‐described by the conventionally used spherical harmonics model that has the maximum order constrained by the number of probes. The objective of this work was to develop and validate a field monitoring approach that compresses higher order spherical harmonics into a smaller set of new basis functions that can be characterized using fewer probes. Methods: Field monitoring of acquisitions was repeated with probes in different locations. High‐order field dynamics were computed from this “calibration” data assembled from provided scans, from which compression matrices could be devised using principal component analysis. Compression matrices were then used to fit field dynamics using “compressed” basis functions with data from 16 probes, which were then used in image reconstruction. Performance was evaluated by assessing the accuracy of computed field dynamics as well as in vivo image quality. Technique generalizability was also assessed by using various acquisition and diffusion encoding strategies in the calibration. Results: Qualitative and quantitative improvements in accuracy were observed when using the proposed fitting method compared to the conventional approach. However, compression effectiveness was influenced by the probe quantity and arrangement, and the specific acquisition data included in the calibration. Conclusion: The ability to tailor basis functions to more compactly describe the spatial variation of field perturbations enables improved characterization of fields with rapid spatial variations.

Risk attitudes – the willingness to accept greater uncertainty to achieve larger potential rewards – determine many aspects of our lives and are often interpreted as an individual trait that reflects a general ‘taste’ for risk. However, this perspective cannot explain why the choices that people make concerning risky outcomes can change considerably across contexts and even across repetitions of the identical decisions. Here we provide modelling and neural evidence that such rapid changes in risk attitudes emerge from Bayesian inference on noisy magnitude representations in the parietal cortex. Our participants underwent fMRI while choosing between safe and risky options that were either held in working memory or present on the screen. Risky options that were held in working memory were less likely to be chosen (risk aversion) when they had relatively large payoffs but more likely to be chosen (risk-seeking) when they had relatively small payoffs. These counterintuitive effects can be explained mechanistically by a computational model of the Bayesian inference underlying payoff perception: Options kept in working memory are noisier and therefore more prone to central tendency biases, leading small (or large) payoffs to be overestimated (or underestimated) more. Congruent with the behavioural modelling, fMRI population-receptive field modelling showed that on trials where intraparietal payoff representations were noisier, choices were also less consistent and less risk-neutral, in line with participants resorting more to their prior beliefs about potential payoffs. Our results highlight that individual risk attitudes and their puzzling changes across contexts and choice repetitions can be rooted in perceptual inference on noisy parietal magnitude representations, with profound implications for economic, psychological, and neuroscience theories of risky behaviour.

Purpose: To evaluate the utility of up to second‐order motion‐compensated diffusion encoding in multi‐shot human brain acquisitions.Methods: Experiments were performed with high‐performance gradients using three forms of diffusion encoding motion‐compensated through different orders: conventional zeroth‐order–compensated pulsed gradients (PG), first‐order–compensated gradients (MC1), and second‐order–compensated gradients (MC2). Single‐shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi‐shot acquisitions were performed at varying interleaving factors. Multi‐shot images were reconstructed using three levels of shot‐to‐shot phase correction: no correction, channel‐wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE).Results: In single‐shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi‐shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high‐quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator‐corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion‐compensated diffusion sensitization was substantially more tolerable than that faced previously.Conclusion: Second‐order motion‐compensated diffusion encoding mitigates and simplifies shot‐to‐shot phase variability in the human brain, rendering the multi‐shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods.